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Aroma in food plays an important role in food perception and acceptance, which depends on various mixtures of volatile organic compounds (VOCs). Moreover, VOCs are of great significance for aroma identification. In this study, headspace solid-phase microextraction (HS-SPME) combined with gas chromatography–mass spectrometry (GC-MS) technology was used to determine the VOCs in 10 pear syrups. A total of 127 VOCs were quantitatively determined, including 9 common VOCs and 46 characteristic VOCs of 10 pear syrups. The pear syrups were divided into three categories by cluster analysis, and thirty-eight differential VOCs were obtained using orthogonal partial least squares discrimination analysis (OPLS-DA) and fourteen key VOCs were selected by odor activity value (OAV). It was revealed that the key and common aroma components of pear syrups were butanoic acid, methyl ester, 2-methyl-, methyl ester and Hexanoic acid, and ethyl ester. The characteristic and differential VOCs were 10-Undecen-1-ol, Hexadecanal, n-Propylacetate, Cyclohexanol, 5-methyl-2-(1-methylethyl)-, (1S,2R,5S)-, Methional, Disulfide, dimethyl, 8-Nonenoic acid, ethyl ester, Naphthalene, 1,2-dihydro-1,1,6-trimethyl-, 3H-Purin-6-amine, N,N,3-trimethyl-, 2-Octanol,2,6-dimethyl-, Furyl hydroxymethyl ketone, Heptane, 2,2,4,6,6-pentamethyl-, and Butanoic acid,2-methyl-,methyl ester. The above results showed that different pear syrups had rich diversity in aroma compounds, with some components being shared among them while others are exclusive to specific syrups. [ABSTRACT FROM AUTHOR]

Background: Infertility is a global reproductive health burden. Assisted reproductive technologies (ARTs) have been widely used to help patients become pregnant. Few embryos develop to the blastocyst stage with ARTs, leading to relatively low live birth rates. Protein modifications play crucial roles in nearly every aspect of cell biology, including reproductive processes. The aim of this study was to explore the characteristics of protein modifications during embryonic development. Methods: Proteomic data from humans and mice were acquired from the integrated proteome resources (iProX) of ProteomeXchange (PXD024267) and a tandem mass tag (TMT)‐mass spectrometry dataset. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied for functional annotation. Protein–protein interactions (PPIs) of the modification‐related genes were revealed by the STRING database. Modified proteins during mouse embryogenesis were visualized through heatmaps of hierarchically clustering using k‐means. Results: We identified modification‐related proteins in human embryo development and characterized them through heatmaps, GO analysis, KEGG analysis, and PPI network analysis. We found that the 4‐cell stage to the 8‐cell stage might be the demarcation period for modification‐related protein expression patterns during embryo development. Using quantitative mass spectrometry, we elucidated the methylation, acetylation, and ubiquitination events that occur during mouse embryogenesis to validate our findings in human embryonic development to some extent. Conclusions: The results of our study suggest that the posttranslational modifications (PTMs) of human preimplantation embryos might exhibit the same trends as those in mice to exert synergistic and fine‐tuned regulatory effects during embryonic development. [ABSTRACT FROM AUTHOR]

Contribution: The design of an operating system (OS) experiment course with a gentle learning curve is proposed and a scalable OS experiment platform supporting learning behavior analysis is presented. Background: In the teaching practice of the OS experiment course, several problems were faced. First, the learning curve for the students is too steep. Second, the OS experiment is hard to scale to a large number of students. Finally, it is difficult to track the learning behaviors of the students and to provide feedback to the teaching plan. Intended Outcomes: By smoothing the learning curve and providing targeted and rapid feedback, students using the system can more effectively master the knowledge of the OS by completing experimental tasks. Application Design: A large number of students can log in to the experiment system and complete multiple labs online. In addition to using automated testing to provide fast feedback, the lab system also collects learning behavior data of the students, enabling the teachers to analyze the data and adjust their teaching plan accordingly. Findings: The performance and learning-behavior data of the students from 2015 to 2018 showed that the OS experiment is effective to scale to a large number of students with satisfiable teaching effectiveness.

Fault localization (FL) and automated program repair (APR) are two main tasks of automatic software debugging. Compared with traditional methods, deep learning-based approaches have been demonstrated to achieve better performance in FL and APR tasks. However, the existing deep learning-based FL methods ignore the deep semantic features or only consider simple code representations. And for APR tasks, existing template-based APR methods are weak in selecting the correct fix templates for more effective program repair, which are also not able to synthesize patches via the embedded end-to-end code modification knowledge obtained by training models on large-scale bug-fix code pairs. Moreover, in most of FL and APR methods, the model designs and training phases are performed separately, leading to ineffective sharing of updated parameters and extracted knowledge during the training process. This limitation hinders the further improvement in the performance of FL and APR tasks. To solve the above problems, we propose a novel approach called MTL-TRANSFER, which leverages a multi-task learning strategy to extract deep semantic features and transferred knowledge from different perspectives. First, we construct a large-scale open-source bug datasets and implement 11 multi-task learning models for bug detection and patch generation sub-tasks on 11 commonly used bug types, as well as one multi-classifier to learn the relevant semantics for the subsequent fix template selection task. Second, an MLP-based ranking model is leveraged to fuse spectrum-based, mutation-based and semantic-based features to generate a sorted list of suspicious statements. Third, we combine the patches generated by the neural patch generation sub-task from the multi-task learning strategy with the optimized fix template selecting order gained from the multi-classifier mentioned above. Finally, the more accurate FL results, the optimized fix template selecting order, and the expanded patch candidates are combined together to further enhance the overall performance of APR tasks. Our extensive experiments on widely-used benchmark Defects4J show that MTL-TRANSFER outperforms all baselines in FL and APR tasks, proving the effectiveness of our approach. Compared with our previously proposed FL method TRANSFER-FL (which is also the state-of-the-art statement-level FL method), MTL-TRANSFER increases the faults hit by 8/11/12 on Top-1/3/5 metrics (92/159/183 in total). And on APR tasks, the number of successfully repaired bugs of MTL-TRANSFER under the perfect localization setting reaches 75, which is 8 more than our previous APR method TRANSFER-PR. Furthermore, another experiment to simulate the actual repair scenarios shows that MTL-TRANSFER can successfully repair 15 and 9 more bugs (56 in total) compared with TBar and TRANSFER, which demonstrates the effectiveness of the combination of our optimized FL and APR components. [ABSTRACT FROM AUTHOR]

In the process of open-pit coal mine production, mining and transport loss of excavator accessories, iron and other large ferromagnetic objects with the coal into the crusher, will result in crusher jammed, broken tooth damage and even motor burn down and other serious production accidents, based on the principle of low frequency eddy current detection can effectively detect ferromagnetic objects, the characteristics of the ferromagnetic objects open-pit mine secondary crushing plant movement monitoring research. Taking SANDVIK's CR610/14—35 double-tooth roller sieve crusher as the experimental object, the mechanism of the detection system was analytically modeled according to the principle of electromagnetic mutual induction. The results of modeling and experiments show that the eddy current generated by ferromagnetic objects is weak due to the existence of strong background noise. The signal to noise ratio(SNR)can be improved by optimizing the parameters of the detector and planning the path of the detector. According to the characteristics of the experimental object, the maximum SNR was obtained when the ratio of the radius of the metal to the detection distance was 0.048, and the ratio of the coil radius to the detection distance was 0.044 5. In the 1~35 kHz segment, the SNR is higher in the low frequency band than the high frequency band. The initial value and response time of the path entering the detection area perpendicular to the detection axis have obvious advantages over the path entering the detection area parallel to the axis.